GIS 4035: Module 5a - Introduction to ERDAS Imagine

This week's assignment was a basic introduction to ERDAS Imagine, a software package designed for doing remote sensing work, specifically with the editing of raster images in mind. While going through the ins and outs of uploading various rasters, a subset of a larger Land Cover raster was exported from Imagine (after adding an area attribute within the software) which was then used to produce the map below in ArcGIS (thus avoiding an avowed bug in Imagine that would have crashed the system when attempting similar output).

Land Cover Map of a Random Portion of northwest Washington state.

GIS4035: Module 4 - Ground Truthing and Accuracy Assessment

This week's assignment built upon last week's. The Land Use Land Cover assessment that was compiled in Module 3 was assessed in this module. Since actual fieldwork is impracticable for an online course, Google Street Map was the next best thing. Thirty random points were generated in the study area, weighted by land classification so that each class used in the classification received at least one point and proportionally more points were assigned to classes with larger coverage areas. Each point was then located in google maps and the accuracy of the classification at that point itself was assessed and recorded. The map below was then compiled, showing the accuracy result for each point.

The accuracy calculated as a ratio of accurate point classifications to the total number of points assessed is 63.3%.

Accuracy Assessment for Pascagoula, MS Land Use / Land Cover Map

GIS4035: Module 3 - Land Use and Land Cover Classification

This week's assignment was to create a land use and land cover classification for an aerial image that was provided. The study area is a portion of Pascagoula, MS including a good portion of wetlands and open water. We were provided with a basic classification scheme based on the one used by the US Geological Survey and had to identify various land types and create polygons to mark off the various classes as we perceived them on the aerial image. Care had to be taken to keep all the classes at the same scale.

Land Use & Land Cover Map for Pascagoula, MS

Special Topics - Network Analysis & Route Generation

Continuing on from the previous week, this portion of the project involved taking the prepared data files - especially the roads feature dataset that had flood areas, drive times and distances added to its attributes - and generating a network dataset from them, i.e. a dataset that can be used to generate routes from one location within the network to another with minimized costs (usually in distance or time) to find optimal routes. In this case, once the dataset was generated, routes were found for the purposes of evacuating a hospital and also routes were found to be used for delivery of relief supplies to designated hurricane shelters across the city. These routes were generated using expected flood areas as a restriction, although routes for emergency personnel were generated that could move through flood zones, but with a high avoidance factor.

In addition to these routes, the network dataset was also used to generate service areas representing which shelters were closest for any particular portion of the road network across the network. These were shown as polygons around each shelter area, where any part of the network within a particular polygon was closest to the shelter location contained within it.

The map below shows all the routes and the service area generated during this week's analysis. Further routes could be generated at need. Also shown are hospitals, fire and police stations.

Basic Tampa Hurricane Evacuation Map

GIS4035: Module 2 - Introduction to Visual Interpretation.

This week's assignment acts as a basic introduction to performing visual interpretation of aerial imagery. Two digitized aerial photographs were provided which were subjected to some basic interpretations. The first image was used to select areas based on tone from very light to very dark and then also based on texture from very smooth to very coarse. The second image was used to identify features in the image based on various attributes, such as the shape and size of an object, and object's shadow, patterns and associations.

Tone and Texture Identification

Feature Identification

Special Topics - Data Preparation for Network Analyst

This is just the first part of a multi-week project. The conceit of the project is that optimal evacuation routes need to be generated for the Tampa, Florida metro area just days ahead of an expected hurricane. These routes will then be produced in maps for use by both the general public and by emergency personnel. This week's work was simply to create the base map from which a network dataset will be generated that will be used to create the routes in upcoming weeks. This involved prepping feature datasets for that upcoming work and also generating a basic map that shows the location of emergency services and also the expected flood zone of the area. This prep also included creating attributes in feature datasets that will be used for generating a network dataset.

Base Map of Tampa Metro Area with Expected Flood Zone & Emergency Services